Pub Date : 2026-01-01DOI: 10.1016/j.phro.2026.100902
Caisa Kjellström , Tobias Pommer , Peter Siesjö , Sofie Ceberg , Per Munck af Rosenschöld
Background and purpose
Stereotactic radiosurgery (SRS) requires high positional accuracy to safely deliver large doses. This study evaluated an integrated surface- and image-guided radiotherapy (SGRT-IGRT) system by analysing (1) the agreement between thermo-optical and stereoscopic X-ray positioning, and (2) the impact of intra-fractional workflows on treatment accuracy and time.
Materials and methods
Data from 126 SRS patients treated with 30 Gy/3 fractions (n = 116) or 12 Gy/1 fraction (n = 10) on a Varian Truebeam STx were retrospectively analysed. Patients were positioned and monitored with Brainlab ExacTrac Dynamic, with 0.5 mm/0.5° tolerances for IGRT and 1 mm/1° for SGRT. Three workflows were investigated: (A) SGRT + IntraArc IGRT (imaging every 90° during treatment and between couch rotations); (B) SGRT + InterArc IGRT (imaging between couch rotations only); and (C) SGRT (no additional imaging after initial coplanar setup). Workflows (B) and (C) were simulated by omitting applied couch corrections.
Results
Median beam-on times were 5.5 min for workflow A, 5.0 min for workflow B, and 3.2 min for workflow C. The median differences between thermo-optical and stereoscopic X-ray patient positioning were ≤0.1 mm. The 3D positioning uncertainty remained within 0.5 mm (2.5th-97.5th percentile) using SGRT-IGRT. Omitting inter-arc imaging increased positional deviation ranges from 0.1-0.5 mm to 0.1–0.7 mm.
Conclusion
Thermo-optical and stereoscopic X-ray imaging showed good agreement within the set institutional tolerances. Inter-arc imaging increased treatment time by 2 min compared with SGRT alone but improved positioning accuracy. Intra-arc imaging added an additional small accuracy benefit at minor time cost.
{"title":"Improvement in positional accuracy with integrated surface- and X-ray imaging for intracranial stereotactic radiosurgery patients","authors":"Caisa Kjellström , Tobias Pommer , Peter Siesjö , Sofie Ceberg , Per Munck af Rosenschöld","doi":"10.1016/j.phro.2026.100902","DOIUrl":"10.1016/j.phro.2026.100902","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Stereotactic radiosurgery (SRS) requires high positional accuracy to safely deliver large doses. This study evaluated an integrated surface- and image-guided radiotherapy (SGRT-IGRT) system by analysing (1) the agreement between thermo-optical and stereoscopic X-ray positioning, and (2) the impact of intra-fractional workflows on treatment accuracy and time.</div></div><div><h3>Materials and methods</h3><div>Data from 126 SRS patients treated with 30 Gy/3 fractions (n = 116) or 12 Gy/1 fraction (n = 10) on a Varian Truebeam STx were retrospectively analysed. Patients were positioned and monitored with Brainlab ExacTrac Dynamic, with 0.5 mm/0.5° tolerances for IGRT and 1 mm/1° for SGRT. Three workflows were investigated: (A) <em>SGRT + IntraArc IGRT</em> (imaging every 90° during treatment and between couch rotations); (B) <em>SGRT + InterArc IGRT</em> (imaging between couch rotations only); and (C) <em>SGRT</em> (no additional imaging after initial coplanar setup). Workflows (B) and (C) were simulated by omitting applied couch corrections.</div></div><div><h3>Results</h3><div>Median beam-on times were 5.5 min for workflow A, 5.0 min for workflow B, and 3.2 min for workflow C. The median differences between thermo-optical and stereoscopic X-ray patient positioning were ≤0.1 mm. The 3D positioning uncertainty remained within 0.5 mm (2.5th-97.5th percentile) using SGRT-IGRT. Omitting inter-arc imaging increased positional deviation ranges from 0.1-0.5 mm to 0.1–0.7 mm.</div></div><div><h3>Conclusion</h3><div>Thermo-optical and stereoscopic X-ray imaging showed good agreement within the set institutional tolerances. Inter-arc imaging increased treatment time by 2 min compared with SGRT alone but improved positioning accuracy. Intra-arc imaging added an additional small accuracy benefit at minor time cost.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100902"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional dose-volume histogram (DVH) metrics used in radiotherapy plan evaluation lack spatial information and are sensitive to organ volume variations. This study investigated the use of Dose Gradient Curves (DGCs) as a robust, volume-independent alternative for assessing organ sparing in prostate stereotactic body radiation therapy (SBRT).
Materials and methods
Treatment plans of 154 prostate cancer patients were retrospectively analysed. A benchmark set of 20 high-quality plans was established, and average DVH (aDVH) and DGC (aDGC) curves were derived for the bladder and anorectum. Plan quality of the remaining 134 plans was assessed using aDVH, aDGC, and expert-reviewed ground truth. A ΔAUC-based classifier was developed to automatically detect suboptimal organ sparing. The robustness of benchmark set size was evaluated by comparing subsets of five plans with extreme organ volumes.
Results
The inclusion of dose-gradient information improved accuracy and precision compared to DVH-based methods. For the bladder, DGC analysis achieved 99% accuracy and precision, compared to 87% and 94% for DVH. For the anorectum, DGC yielded 97% accuracy and 100% precision. The ΔAUC classifier achieved F1 scores of 97.1% (bladder) and 89.7% (anorectum). Reducing the benchmark set to five plans did not significantly affect DGC-based evaluations, unlike DVH-based assessments.
Conclusions
DGC-based plan evaluation offers a reliable and volume-independent method for assessing organ sparing in prostate SBRT. It enabled automated detection of suboptimal plans and remained robust even with reduced benchmark sizes. Further investigation prior to clinical implementation is required.
{"title":"Automated evaluation of organ sparing in prostate stereotactic body radiation therapy using dose-gradient metrics","authors":"Geert De Kerf , Michaël Claessens , Thibaut D’homme , Piet Dirix , Piet Ost , Dirk Verellen","doi":"10.1016/j.phro.2026.100911","DOIUrl":"10.1016/j.phro.2026.100911","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Traditional dose-volume histogram (DVH) metrics used in radiotherapy plan evaluation lack spatial information and are sensitive to organ volume variations. This study investigated the use of Dose Gradient Curves (DGCs) as a robust, volume-independent alternative for assessing organ sparing in prostate stereotactic body radiation therapy (SBRT).</div></div><div><h3>Materials and methods</h3><div>Treatment plans of 154 prostate cancer patients were retrospectively analysed. A benchmark set of 20 high-quality plans was established, and average DVH (aDVH) and DGC (aDGC) curves were derived for the bladder and anorectum. Plan quality of the remaining 134 plans was assessed using aDVH, aDGC, and expert-reviewed ground truth. A ΔAUC-based classifier was developed to automatically detect suboptimal organ sparing. The robustness of benchmark set size was evaluated by comparing subsets of five plans with extreme organ volumes.</div></div><div><h3>Results</h3><div>The inclusion of dose-gradient information improved accuracy and precision compared to DVH-based methods. For the bladder, DGC analysis achieved 99% accuracy and precision, compared to 87% and 94% for DVH. For the anorectum, DGC yielded 97% accuracy and 100% precision. The ΔAUC classifier achieved F1 scores of 97.1% (bladder) and 89.7% (anorectum). Reducing the benchmark set to five plans did not significantly affect DGC-based evaluations, unlike DVH-based assessments.</div></div><div><h3>Conclusions</h3><div>DGC-based plan evaluation offers a reliable and volume-independent method for assessing organ sparing in prostate SBRT. It enabled automated detection of suboptimal plans and remained robust even with reduced benchmark sizes. Further investigation prior to clinical implementation is required.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100911"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.phro.2025.100900
Francesco Fracchiolla , Arturs Meijers , Ester Orlandi , Erik Korevaar , Gillian Whitfield , Kenneth Jensen , Mischa Hoogeman , Robin Wijsman , Emmanuel Jouglar , Eva Van Weerd , Juan M. Pérez , Ilaria Rinaldi , Magdalena Garbacz-Stryszewska , Marco Cianchetti , Marta Montero Feijoo , Silvia Molinelli , Ulrik Vindelev Elstrøm , Alessia Pica , Andrew Gosling , Beata Koczur , Lamberto Widesott
Background and purpose
Robustness evaluation (RE) is vital for proton treatment planning, but lacks international consensus or guidelines, with clinics using varied, self-developed methods focused on selected uncertainties. This ESTRO project surveys expert opinions on clinical RE methods to inform future treatment planning system (TPS) development.
Materials and methods
A study within the European Particle Therapy Network (EPTN) involved 24 European proton therapy centres, with one radiation oncologist and one medical physicist per centre. The goal was to reach a consensus on transitioning from Planning Target Volume (PTV)-based planning to robustly optimized planning, including uncertainties, methods, and reporting of robustness evaluations. An internal committee drafted 39 statements, reviewed by an independent committee. Following a two-round Delphi procedure, consensus was set at a 75% agreement threshold.
Results
Twenty of 24 contacted centers (83.0%) responded to both questionnaire rounds. Consensus was reached on 26 of 39 statements (66.7%), with 5 being high-priority. Strong agreement emerged regarding which uncertainties to include in RE (range, setup, intra-fraction, anatomy changes), methodologies (e.g., for moving targets, combining setup and range), and how to report RE results clinically. Disagreement was found on using the PTV for both planning and dose reporting. The results also offer important implications for TPS vendors and future software development.
Conclusions
The ESTRO Delphi consensus may serve as practical guidance on points where a clear consensus was achieved. For remaining points, the development of guidelines is recommended to standardize methodologies and reporting. Furthermore, TPS vendors are encouraged to align their developments with the community’s articulated requirements.
{"title":"An ESTRO-EPTN Delphi consensus on robustness evaluation in proton therapy","authors":"Francesco Fracchiolla , Arturs Meijers , Ester Orlandi , Erik Korevaar , Gillian Whitfield , Kenneth Jensen , Mischa Hoogeman , Robin Wijsman , Emmanuel Jouglar , Eva Van Weerd , Juan M. Pérez , Ilaria Rinaldi , Magdalena Garbacz-Stryszewska , Marco Cianchetti , Marta Montero Feijoo , Silvia Molinelli , Ulrik Vindelev Elstrøm , Alessia Pica , Andrew Gosling , Beata Koczur , Lamberto Widesott","doi":"10.1016/j.phro.2025.100900","DOIUrl":"10.1016/j.phro.2025.100900","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Robustness evaluation (RE) is vital for proton treatment planning, but lacks international consensus or guidelines, with clinics using varied, self-developed methods focused on selected uncertainties. This ESTRO project surveys expert opinions on clinical RE methods to inform future treatment planning system (TPS) development.</div></div><div><h3>Materials and methods</h3><div>A study within the European Particle Therapy Network (EPTN) involved 24 European proton therapy centres, with one radiation oncologist and one medical physicist per centre. The goal was to reach a consensus on transitioning from Planning Target Volume (PTV)-based planning to robustly optimized planning, including uncertainties, methods, and reporting of robustness evaluations. An internal committee drafted 39 statements, reviewed by an independent committee. Following a two-round Delphi procedure, consensus was set at a 75% agreement threshold.</div></div><div><h3>Results</h3><div>Twenty of 24 contacted centers (83.0%) responded to both questionnaire rounds. Consensus was reached on 26 of 39 statements (66.7%), with 5 being high-priority. Strong agreement emerged regarding which uncertainties to include in RE (range, setup, intra-fraction, anatomy changes), methodologies (e.g., for moving targets, combining setup and range), and how to report RE results clinically. Disagreement was found on using the PTV for both planning and dose reporting. The results also offer important implications for TPS vendors and future software development.</div></div><div><h3>Conclusions</h3><div>The ESTRO Delphi consensus may serve as practical guidance on points where a clear consensus was achieved. For remaining points, the development of guidelines is recommended to standardize methodologies and reporting. Furthermore, TPS vendors are encouraged to align their developments with the community’s articulated requirements.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100900"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.phro.2026.100904
Sarra Midani , Paul Retif , Sébastien Maksimovic , Clémence Bondue , Mohammed Yacoubi , Gianandrea Pietta , Anwar Al Salah , Estelle Pfletschinger , Motchy Saleh , Abdourahamane Djibo Sidikou , Romain Letellier , Fabian Taesch , Emilie Verrecchia-Ramos , Xavier Michel
Purpose
To evaluate the feasibility, dosimetric quality, workflow efficiency, and early tolerance of automated deep-inspiration breath-hold (DIBH) breast radiotherapy delivered on a ring-gantry platform.
Materials and methods
Twenty patients requiring locoregional irradiation were treated on a Radixact ring-gantry system between February and September 2025 using a static-beam intensity-modulated technique in automated DIBH. Dose/volume metrics for targets and organs of interest (OOIs), workflow parameters, and acute side effects were collected. Benchmark helical tomotherapy plans in DIBH conditions were reoptimized for comparison.
Results
All patients completed DIBH treatment. PTV coverage was consistently achieved (mean V95%: 97.2% for low-risk and 99.2% for boost volumes) and OOI objectives were met. Daily image acquisition required 20–32 s. Median expected beam-on time was 230 s, while delivered beam-on time was 416 s. Median fraction duration was approximately 10 min, including setup, imaging and delivery. A total of 3511 gated beam segments were recorded (median duration 1.8 s), confirming reproducibility and patient compliance. Compared with helical delivery in a theoretical DIBH scenario, static-beam IMRT method reduced contralateral exposure, while helical delivery yielded slightly lower cardiac doses; planned beam-on times were significantly longer with helical mode (+54%). Acute side effects were limited to grade 1 (60%) or 2 (10%) dermatitis and grade 1 esophagitis (15%), with no grade ≥3 events at median 2 months.
Conclusions
Fully automated DIBH breast radiotherapy on a ring-based accelerator is feasible, safe and compatible with routine workflow. This study provides the first experience supporting automated DIBH gated delivery on a ring-based accelerator.
{"title":"Feasibility and workflow efficiency of automated deep inspiration breath-hold for locoregional breast irradiation on a ring-gantry accelerator","authors":"Sarra Midani , Paul Retif , Sébastien Maksimovic , Clémence Bondue , Mohammed Yacoubi , Gianandrea Pietta , Anwar Al Salah , Estelle Pfletschinger , Motchy Saleh , Abdourahamane Djibo Sidikou , Romain Letellier , Fabian Taesch , Emilie Verrecchia-Ramos , Xavier Michel","doi":"10.1016/j.phro.2026.100904","DOIUrl":"10.1016/j.phro.2026.100904","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the feasibility, dosimetric quality, workflow efficiency, and early tolerance of automated deep-inspiration breath-hold (DIBH) breast radiotherapy delivered on a ring-gantry platform.</div></div><div><h3>Materials and methods</h3><div>Twenty patients requiring locoregional irradiation were treated on a Radixact ring-gantry system between February and September 2025 using a static-beam intensity-modulated technique in automated DIBH. Dose/volume metrics for targets and organs of interest (OOIs), workflow parameters, and acute side effects were collected. Benchmark helical tomotherapy plans in DIBH conditions were reoptimized for comparison.</div></div><div><h3>Results</h3><div>All patients completed DIBH treatment. PTV coverage was consistently achieved (mean V<sub>95%</sub>: 97.2% for low-risk and 99.2% for boost volumes) and OOI objectives were met. Daily image acquisition required 20–32 s. Median expected beam-on time was 230 s, while delivered beam-on time was 416 s. Median fraction duration was approximately 10 min, including setup, imaging and delivery. A total of 3511 gated beam segments were recorded (median duration 1.8 s), confirming reproducibility and patient compliance. Compared with helical delivery in a theoretical DIBH scenario, static-beam IMRT method reduced contralateral exposure, while helical delivery yielded slightly lower cardiac doses; planned beam-on times were significantly longer with helical mode (+54%). Acute side effects were limited to grade 1 (60%) or 2 (10%) dermatitis and grade 1 esophagitis (15%), with no grade ≥3 events at median 2 months.</div></div><div><h3>Conclusions</h3><div>Fully automated DIBH breast radiotherapy on a ring-based accelerator is feasible, safe and compatible with routine workflow. This study provides the first experience supporting automated DIBH gated delivery on a ring-based accelerator.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100904"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.phro.2025.100893
Alessandro Bombini , Luca Vellini , Flaviovincenzo Quaranta , Jacopo Lenkowicz , Sebastiano Menna , Elisa Pilloni , Francesco Catucci , Andrea D’Aviero , Claudio Votta , Giuditta Chiloiro , Martina Iezzi , Francesco Preziosi , Alessia Re , Althea Boschetti , Floranna Mauro , Sami Aburas , Lana Smiljanic , Antonio Piras , Carmela Di Dio , Lorenzo Placidi , Davide Cusumano
Background and purpose
Magnetic Resonance Imaging-only (MRI-only) workflows are an emerging strategy in radiotherapy, with artificial intelligence (AI) playing a central role in generating synthetic computed tomography (sCT) images. The thorax remains a particularly difficult region due to marked electron density (ED) heterogeneity and respiratory motion. This study investigates the impact of key factors on AI-based thoracic sCT generation.
Materials and methods
A total of 122 thoracic patients treated with MRI-guided radiotherapy (MRIgRT) were retrospectively included. Both 0.35 Tesla (T) MR and CT simulation images were acquired under consistent breath-hold conditions. Three aspects were analyzed: (i) training set size (34, 68, and 102 cases), (ii) pre-processing of MR images (filtered versus raw), and (iii) generator architecture, comparing U-Net and ResNet with a novel model integrating Fourier space information, the Adaptive Fourier Neural Operator (AFNO). Models were tested on 20 independent patients using image similarity metrics. The best configuration was also evaluated through dose recalculations.
Results
Expanding the training set improved accuracy, reducing Mean Absolute Error (MAE) from 42.0 ± 9 Hounsfield Units (HU) to 35.9 ± 6 HU. Pre-processing had limited effect, while generator architecture had a strong impact, with AFNO outperforming others (MAE = 32.4 ± 6 HU). The optimal setup, AFNO trained on raw MR images from 102 patients, yielded dosimetric deviations below 3 % for target dose-volume metrics and within 50 cGy for organs at risk (OARs).
Conclusions
These findings highlight the importance of training dataset size and advanced network architectures for thoracic sCT generation. AFNO demonstrated superior performance, reinforcing the feasibility of MRI-only workflows in thoracic radiotherapy.
{"title":"Optimizing thoracic synthetic computed tomography generation from magnetic resonance imaging: the role of Fourier transform and other key factors","authors":"Alessandro Bombini , Luca Vellini , Flaviovincenzo Quaranta , Jacopo Lenkowicz , Sebastiano Menna , Elisa Pilloni , Francesco Catucci , Andrea D’Aviero , Claudio Votta , Giuditta Chiloiro , Martina Iezzi , Francesco Preziosi , Alessia Re , Althea Boschetti , Floranna Mauro , Sami Aburas , Lana Smiljanic , Antonio Piras , Carmela Di Dio , Lorenzo Placidi , Davide Cusumano","doi":"10.1016/j.phro.2025.100893","DOIUrl":"10.1016/j.phro.2025.100893","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Magnetic Resonance Imaging-only (MRI-only) workflows are an emerging strategy in radiotherapy, with artificial intelligence (AI) playing a central role in generating synthetic computed tomography (sCT) images. The thorax remains a particularly difficult region due to marked electron density (ED) heterogeneity and respiratory motion. This study investigates the impact of key factors on AI-based thoracic sCT generation.</div></div><div><h3>Materials and methods</h3><div>A total of 122 thoracic patients treated with MRI-guided radiotherapy (MRIgRT) were retrospectively included. Both 0.35 Tesla (T) MR and CT simulation images were acquired under consistent breath-hold conditions. Three aspects were analyzed: (i) training set size (34, 68, and 102 cases), (ii) pre-processing of MR images (filtered versus raw), and (iii) generator architecture, comparing U-Net and ResNet with a novel model integrating Fourier space information, the Adaptive Fourier Neural Operator (AFNO). Models were tested on 20 independent patients using image similarity metrics. The best configuration was also evaluated through dose recalculations<strong>.</strong></div></div><div><h3>Results</h3><div>Expanding the training set improved accuracy, reducing Mean Absolute Error (MAE) from 42.0 ± 9 Hounsfield Units (HU) to 35.9 ± 6 HU. Pre-processing had limited effect, while generator architecture had a strong impact, with AFNO outperforming others (MAE = 32.4 ± 6 HU). The optimal setup, AFNO trained on raw MR images from 102 patients, yielded dosimetric deviations below 3 % for target dose-volume metrics and within 50 cGy for organs at risk (OARs).</div></div><div><h3>Conclusions</h3><div>These findings highlight the importance of training dataset size and advanced network architectures for thoracic sCT generation. AFNO demonstrated superior performance, reinforcing the feasibility of MRI-only workflows in thoracic radiotherapy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100893"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.phro.2026.100901
Linus A. Carizzoni , Alexey Cherchik , Xia Li , Antony Lomax , Ye Zhang
Background and purpose
The robustness of pencil beam scanned (PBS) proton plans to respiratory motion is often assessed in clinical practice by static 4D dose recalculations on selected 4D computed tomography (4DCT) phases. These capture anatomical variation but neglect interplay effects from sequential beam delivery. This study investigates these effects by comparing static and dynamic 4DDC for esophageal cancer patients.
Materials and methods
PBS proton plans following the PROTECT trial protocol were created for ten esophageal cancer patients from the open-access DIR-Lab 4DCT dataset. Plan robustness was evaluated by static and dynamic 4DDC, where the static approach accumulated the computed dose in individual 4DCT phases, while dynamic incorporated the temporal delivery sequence to capture interplay effects. The two 4DDCs were compared by their compliance to the dose restrictions for target volumes and organs at risk (OARs)
Results
Static 4DDC consistently predicted higher target coverage than dynamic approach. Discrepancies were most pronounced in patients with substantial target motion (≳10 mm). However, dose metrics for the OARs showed high agreement between the two methods. Compliance with the clinical constraint on target coverage (V95%>97 %) was achieved in 100 % and 70 % of static and dynamic 4D recalculations. Rescanning improved the compliance of target coverage to 90 %.
Conclusion
Protocol-based static 4DDC tended to overestimate target coverage robustness to respiratory motion. Although differences were minor in most cases, patients with large motion can have significant discrepancies, underscoring the importance of implementing dynamic 4DDC in PBS proton planning for esophageal cancer.
{"title":"Comparative evaluation of static and dynamic 4D dose recalculations in pencil beam scanning proton therapy for oesophageal cancer","authors":"Linus A. Carizzoni , Alexey Cherchik , Xia Li , Antony Lomax , Ye Zhang","doi":"10.1016/j.phro.2026.100901","DOIUrl":"10.1016/j.phro.2026.100901","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The robustness of pencil beam scanned (PBS) proton plans to respiratory motion is often assessed in clinical practice by static 4D dose recalculations on selected 4D computed tomography (4DCT) phases. These capture anatomical variation but neglect interplay effects from sequential beam delivery. This study investigates these effects by comparing static and dynamic 4DDC for esophageal cancer patients.</div></div><div><h3>Materials and methods</h3><div>PBS proton plans following the PROTECT trial protocol were created for ten esophageal cancer patients from the open-access DIR-Lab 4DCT dataset. Plan robustness was evaluated by static and dynamic 4DDC, where the static approach accumulated the computed dose in individual 4DCT phases, while dynamic incorporated the temporal delivery sequence to capture interplay effects. The two 4DDCs were compared by their compliance to the dose restrictions for target volumes and organs at risk (OARs)</div></div><div><h3>Results</h3><div>Static 4DDC consistently predicted higher target coverage than dynamic approach. Discrepancies were most pronounced in patients with substantial target motion (≳10 mm). However, dose metrics for the OARs showed high agreement between the two methods. Compliance with the clinical constraint on target coverage (V<sub>95%</sub> <em>></em>97 %) was achieved in 100 % and 70 % of static and dynamic 4D recalculations. Rescanning improved the compliance of target coverage to 90 %.</div></div><div><h3>Conclusion</h3><div>Protocol-based static 4DDC tended to overestimate target coverage robustness to respiratory motion. Although differences were minor in most cases, patients with large motion can have significant discrepancies, underscoring the importance of implementing dynamic 4DDC in PBS proton planning for esophageal cancer.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100901"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.phro.2026.100903
Yifei Pi , Haiyang Wang , Yawei Zhang , Zhao Peng , Xianhu Zeng , Yuexin Guo , Chunbo Liu
Background and purpose
Accurate commissioning of proton beam models remained a major challenge in pencil beam scanning (PBS) proton therapy. This study presented an automated Monte Carlo (MC) modeling framework that was designed to automate and standardize beam model commissioning.
Materials and methods
This framework supported commissioning workflows by optimizing beam parameters based on user-supplied data including integrated depth dose curves, lateral profiles, measured absolute dose per energy, etc. It incorporated optimization algorithms including particle swarm optimization and Nelder-Mead, and followed a modular pipeline including data preparation, phase space parameter fitting, energy spectrum tuning, and dose calibration. Validation was performed using 20 clinical cases and over 100 measurement 2D planes in water-based patient-specific quality assurance (QA) plans. The framework was commissioned with TOol for PArticle Simulation (TOPAS) and Monte Carlo square (MCsquare).
Results
After tuning, both MC engines reproduced maximum range errors of 0.3 % (TOPAS) and 0.6 % (MCsquare) at depths corresponding to 80 % and 20 % of the maximum dose, and similarly small deviations in the full width at half maximum and peak dose. For QA plans, the median gamma pass rate was 100.0 % for TOPAS under the 3 %/3 mm criterion (range: 95.3 %–100.0 %, mean: 99.9 %), with MCsquare achieved comparable results with minimum pass rates above 94.3 %.
Conclusions
This open-source, Python-based framework provided a robust and extensible solution for automated multi-engine MC beam commissioning in proton therapy. It enhanced reproducibility and efficiency, facilitating both clinical and research applications in medical physics.
{"title":"A novel automated framework for multi-engine Monte Carlo model commissioning in proton therapy","authors":"Yifei Pi , Haiyang Wang , Yawei Zhang , Zhao Peng , Xianhu Zeng , Yuexin Guo , Chunbo Liu","doi":"10.1016/j.phro.2026.100903","DOIUrl":"10.1016/j.phro.2026.100903","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Accurate commissioning of proton beam models remained a major challenge in pencil beam scanning (PBS) proton therapy. This study presented an automated Monte Carlo (MC) modeling framework that was designed to automate and standardize beam model commissioning.</div></div><div><h3>Materials and methods</h3><div>This framework supported commissioning workflows by optimizing beam parameters based on user-supplied data including integrated depth dose curves, lateral profiles, measured absolute dose per energy, etc. It incorporated optimization algorithms including particle swarm optimization and Nelder-Mead, and followed a modular pipeline including data preparation, phase space parameter fitting, energy spectrum tuning, and dose calibration. Validation was performed using 20 clinical cases and over 100 measurement 2D planes in water-based patient-specific quality assurance (QA) plans. The framework was commissioned with TOol for PArticle Simulation (TOPAS) and Monte Carlo square (MCsquare).</div></div><div><h3>Results</h3><div>After tuning, both MC engines reproduced maximum range errors of 0.3 % (TOPAS) and 0.6 % (MCsquare) at depths corresponding to 80 % and 20 % of the maximum dose, and similarly small deviations in the full width at half maximum and peak dose. For QA plans, the median gamma pass rate was 100.0 % for TOPAS under the 3 %/3<!--> <!-->mm criterion (range: 95.3 %–100.0 %, mean: 99.9 %), with MCsquare achieved comparable results with minimum pass rates above 94.3 %.</div></div><div><h3>Conclusions</h3><div>This open-source, Python-based framework provided a robust and extensible solution for automated multi-engine MC beam commissioning in proton therapy. It enhanced reproducibility and efficiency, facilitating both clinical and research applications in medical physics.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100903"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.phro.2026.100906
Zeyu Zhang , Dongyang Guo , Ke Lu , Zhuoran Jiang , Hualiang Zhong , Fang-Fang Yin , Lei Ren , Zhenyu Yang
Background and purpose
Accurate registration of pretreatment Magnetic Resonance Imaging (MRI) to onboard Cone Beam Computed Tomography (CBCT) is critical for liver Stereotactic Body Radiation Therapy (SBRT) but is challenged by poor CBCT soft-tissue contrast and respiratory motion. We developed and validated PhysMorph, a physics-informed deep learning framework designed to provide rapid, anatomically plausible MR-CBCT image registration of the liver.
Materials and methods
We developed PhysMorph, a registration framework that incorporated finite element method (FEM) simulations as biomechanical regularization alongside image similarity metrics. The framework was validated on two datasets: (1) simulated data with a known ground-truth deformation derived from longitudinal MR-Linac scans, and (2) clinical MR-CBCT pairs from liver SBRT patients. Performance was assessed using target registration error (TRE), mean surface distance (MSD), and metrics of biomechanical fidelity.
Results
On clinical data, PhysMorph achieved a mean TRE of 2.2 ± 1.4 mm and a MSD of 1.60 ± 0.05 mm, significantly outperforming VoxelMorph (4.11 ± 1.53 mm) and SynthMorph (4.41 ± 1.67 mm) while maintaining high biomechanical fidelity. The framework reduced registration time from over 10 min for conventional finite element methods to 103.4 ms, enabling practical real-time application.
Conclusions
PhysMorph enables fast, accurate, and physically realistic registration of pretreatment MRI to on-board CBCT for liver SBRT. By integrating MRI’s superior soft-tissue visualization while ensuring anatomical plausibility, our approach facilitates precise tumor localization that could enable smaller planning target volumes and more conformal dose distributions, potentially enhancing tumor control while reducing radiation exposure to healthy tissues.
{"title":"PhysMorph: A biomechanical and image-guided deep learning framework for real-time multi-modal liver image registration","authors":"Zeyu Zhang , Dongyang Guo , Ke Lu , Zhuoran Jiang , Hualiang Zhong , Fang-Fang Yin , Lei Ren , Zhenyu Yang","doi":"10.1016/j.phro.2026.100906","DOIUrl":"10.1016/j.phro.2026.100906","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Accurate registration of pretreatment Magnetic Resonance Imaging (MRI) to onboard Cone Beam Computed Tomography (CBCT) is critical for liver Stereotactic Body Radiation Therapy (SBRT) but is challenged by poor CBCT soft-tissue contrast and respiratory motion. We developed and validated PhysMorph, a physics-informed deep learning framework designed to provide rapid, anatomically plausible MR-CBCT image registration of the liver.</div></div><div><h3>Materials and methods</h3><div>We developed PhysMorph, a registration framework that incorporated finite element method (FEM) simulations as biomechanical regularization alongside image similarity metrics. The framework was validated on two datasets: (1) simulated data with a known ground-truth deformation derived from longitudinal MR-Linac scans, and (2) clinical MR-CBCT pairs from liver SBRT patients. Performance was assessed using target registration error (TRE), mean surface distance (MSD), and metrics of biomechanical fidelity.</div></div><div><h3>Results</h3><div>On clinical data, PhysMorph achieved a mean TRE of 2.2 ± 1.4 mm and a MSD of 1.60 ± 0.05 mm, significantly outperforming VoxelMorph (4.11 ± 1.53 mm) and SynthMorph (4.41 ± 1.67 mm) while maintaining high biomechanical fidelity. The framework reduced registration time from over 10 min for conventional finite element methods to 103.4 ms, enabling practical real-time application.</div></div><div><h3>Conclusions</h3><div>PhysMorph enables fast, accurate, and physically realistic registration of pretreatment MRI to on-board CBCT for liver SBRT. By integrating MRI’s superior soft-tissue visualization while ensuring anatomical plausibility, our approach facilitates precise tumor localization that could enable smaller planning target volumes and more conformal dose distributions, potentially enhancing tumor control while reducing radiation exposure to healthy tissues.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100906"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.phro.2026.100907
Chloe Min Seo Choi , Jue Jiang , Nikhil P. Mankuzhy , Nishant Nadkarni , Sudharsan Madhavan , Abraham J. Wu , Joseph O. Deasy , Maria Thor , Andreas Rimner , Harini Veeraraghavan
Background and purpose
Deformable image registration (DIR) for voxel-based analysis (VBA) can be challenging in patients with non-small cell lung cancer (NSCLC) due to large variations in tumor size and location. This study aimed to assess whether a tumor-preserving inter-patient DIR approach improves VBA-based prediction of radiation pneumonitis (RP).
Methods and materials
Three DIR methods were evaluated: deep learning-based Tumor-Aware Recurrent Registration (TRACER) and Patient-Specific Context and Shape (PACS), trained on a public dataset of 268 locally-advanced (LA) NSCLC patients, and iterative Symmetric Normalization (SyN). All methods were tested on 240 patients with LA-NSCLC. Geometric, dosimetric, and tumor preservation metrics were compared using the Wilcoxon signed-rank test. VBA was conducted with each DIR method to identify cohort-relevant regions (CRRs). Machine learning models incorporating clinical, dosimetric, and CRR dose features were used to predict grade 2 or higher RP.
Results
TRACER best preserved tumor volume (1.39 %) and organ doses (mean 0.08 Gy) compared with PACS and SyN (p < 0.001). PACS showed higher geometric but worse dose preservation accuracy than TRACER. All DIR-based VBA methods identified the right lung as the CRR associated with RP. TRACER-derived CRR had slightly higher RP predictive performance (AUC 0.78 vs PACS 0.73 vs SyN 0.71), and outperformed the MLD-based ML model (AUC = 0.78 vs 0.69, p = 0.04; specificity = 0.62 vs 0.48).
Conclusions
TRACER improved registration accuracy, with better tumor volume preservation and reduced OAR dose impact. Incorporating VBA-derived dose enhanced RP prediction accuracy compared with using MLD. CRRs identified through VBA were robust to the choice of DIR.
背景和目的由于肿瘤大小和位置的巨大差异，非小细胞肺癌（NSCLC）患者的基于体素分析（VBA）的可变形图像配准（DIR）可能具有挑战性。本研究旨在评估保留肿瘤的患者间DIR方法是否能改善基于vba的放射性肺炎（RP）预测。方法和材料评估了三种DIR方法：基于深度学习的肿瘤感知复发登记（TRACER）和患者特异性上下文和形状（PACS），在268例局部晚期（LA） NSCLC患者的公共数据集上训练，以及迭代对称归一化（SyN）。所有方法在240例LA-NSCLC患者中进行了测试。使用Wilcoxon符号秩检验比较几何、剂量学和肿瘤保存指标。采用每种DIR方法进行VBA以确定队列相关区域（CRRs）。结合临床、剂量学和CRR剂量特征的机器学习模型用于预测2级或更高级别的RP。结果与PACS和SyN相比，stracer能更好地保存肿瘤体积（1.39%）和器官剂量（平均0.08 Gy） （p < 0.001）。PACS的几何保存精度高于TRACER，但剂量保存精度较差。所有基于dir的VBA方法均将右肺确定为与RP相关的CRR。tracer衍生的CRR具有稍高的RP预测性能（AUC 0.78 vs PACS 0.73 vs SyN 0.71），并且优于基于mld的ML模型（AUC = 0.78 vs 0.69, p = 0.04；特异性= 0.62 vs 0.48）。结论stracer可提高配准精度，更好地保留肿瘤体积，降低OAR剂量影响。与使用MLD相比，结合vba衍生剂量可提高RP预测的准确性。通过VBA识别的crr对DIR的选择具有鲁棒性。
{"title":"Tumor-conditioned inter-patient registration using planning computed tomography for voxel-based analysis to predict radiation pneumonitis in lung cancer patients","authors":"Chloe Min Seo Choi , Jue Jiang , Nikhil P. Mankuzhy , Nishant Nadkarni , Sudharsan Madhavan , Abraham J. Wu , Joseph O. Deasy , Maria Thor , Andreas Rimner , Harini Veeraraghavan","doi":"10.1016/j.phro.2026.100907","DOIUrl":"10.1016/j.phro.2026.100907","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Deformable image registration (DIR) for voxel-based analysis (VBA) can be challenging in patients with non-small cell lung cancer (NSCLC) due to large variations in tumor size and location. This study aimed to assess whether a tumor-preserving inter-patient DIR approach improves VBA-based prediction of radiation pneumonitis (RP).</div></div><div><h3>Methods and materials</h3><div>Three DIR methods were evaluated: deep learning-based Tumor-Aware Recurrent Registration (TRACER) and Patient-Specific Context and Shape (PACS), trained on a public dataset of 268 locally-advanced (LA) NSCLC patients, and iterative Symmetric Normalization (SyN). All methods were tested on 240 patients with LA-NSCLC. Geometric, dosimetric, and tumor preservation metrics were compared using the Wilcoxon signed-rank test. VBA was conducted with each DIR method to identify cohort-relevant regions (CRRs). Machine learning models incorporating clinical, dosimetric, and CRR dose features were used to predict grade 2 or higher RP.</div></div><div><h3>Results</h3><div>TRACER best preserved tumor volume (1.39 %) and organ doses (mean 0.08 Gy) compared with PACS and SyN (p < 0.001). PACS showed higher geometric but worse dose preservation accuracy than TRACER. All DIR-based VBA methods identified the right lung as the CRR associated with RP. TRACER-derived CRR had slightly higher RP predictive performance (AUC 0.78 vs PACS 0.73 vs SyN 0.71), and outperformed the MLD-based ML model (AUC = 0.78 vs 0.69, p = 0.04; specificity = 0.62 vs 0.48).</div></div><div><h3>Conclusions</h3><div>TRACER improved registration accuracy, with better tumor volume preservation and reduced OAR dose impact. Incorporating VBA-derived dose enhanced RP prediction accuracy compared with using MLD. CRRs identified through VBA were robust to the choice of DIR.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100907"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quality Assurance for online adaptive radiotherapy (oART) can be challenging. Several tests can demonstrate the dosimetric and position accuracy, but commercial phantoms are often not anatomically representative. The aim of this study was to investigate the accuracy of cone-beam computed tomography guided oART palliative and breast cancer trials by using a 3D printed thorax anthropomorphic phantom.
Materials and methods
An anthropomorphic phantom was 3D printed for this study which accommodates film through the spine, breast, heart, and lungs. Dose was measured for spine and breast treatment plans, whilst variations were simulated which can occur during treatment. Measurements were compared to calculated dose on the planning (pCT) and synthetic computed tomography (sCT) using gamma pass rate criteria of minimal 95 % (for gamma of 4 %/2 mm). Differences between the mean gamma were tested for significance.
Results
Measurements done with positional and target volume changes showed no significant difference between the gamma analyses for the pCT and sCT (p = 0.15), indicating a robust and safe workflow. For extreme variations, difference was found between gamma analyses for the pCT and sCT (p = 0.051). Pass rates were all >95 %, except for three measurements in which the sCT showed density errors up to 1000 Hounsfield Units.
Conclusions
This QA approach for oART, which used film measurements in a custom 3D-printed anthropomorphic phantom was able to validate the accuracy of the oART workflow when anatomical deviations arise and could be suitable as end-to-end test in the future.
{"title":"Quality assurance of online adaptive radiotherapy workflows using film dosimetry in a 3D printed thorax anthropomorphic phantom","authors":"Daan Hoffmans , Koen Nelissen , Eva Versteijne , Wilko Verbakel","doi":"10.1016/j.phro.2026.100909","DOIUrl":"10.1016/j.phro.2026.100909","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Quality Assurance for online adaptive radiotherapy (oART) can be challenging. Several tests can demonstrate the dosimetric and position accuracy, but commercial phantoms are often not anatomically representative. The aim of this study was to investigate the accuracy of cone-beam computed tomography guided oART palliative and breast cancer trials by using a 3D<!--> <!-->printed thorax anthropomorphic phantom.</div></div><div><h3>Materials and methods</h3><div>An anthropomorphic phantom was 3D<!--> <!-->printed for this study which accommodates film through the spine, breast, heart, and lungs. Dose was measured for spine and breast treatment plans, whilst variations were simulated which can occur during treatment. Measurements were compared to calculated dose on the planning (pCT) and synthetic computed tomography (sCT) using gamma pass rate criteria of minimal 95<!--> <!--> % (for gamma of 4<!--> <!--> %/2<!--> <!-->mm). Differences between the mean gamma were tested for significance.</div></div><div><h3>Results</h3><div>Measurements done with positional and target volume changes showed no significant difference between the gamma analyses for the pCT and sCT (p = 0.15), indicating a robust and safe workflow. For extreme variations, difference was found between gamma analyses for the pCT and sCT (p = 0.051). Pass rates were all >95<!--> <!--> %, except for three measurements in which the sCT showed density errors up to 1000 Hounsfield<!--> <!-->Units.</div></div><div><h3>Conclusions</h3><div>This QA approach for oART, which used film measurements in a custom 3D-printed anthropomorphic phantom was able to validate the accuracy of the oART workflow when anatomical deviations arise and could be suitable as end-to-end test in the future.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100909"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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